Abstract

[Background] One pillar to monitoring progress towards the Sustainable Development Goals is the investment in high quality data to strengthen the scientific basis for decision-making. At present, nationally-representative surveys are the main source of data for establishing a scientific evidence base, monitoring, and evaluation of health metrics. However, little is known about the optimal precisions of various population-level health and development indicators that remains unquantified in nationally-representative household surveys. Here, a retrospective analysis of the precision of prevalence from these surveys was ...

Abstract

Improved understanding of geographic variation and inequity in health status, wealth, and access to resources within countries is increasingly being recognized as central to meeting development goals. Development and health indicators assessed at national scales conceal important inequities, with the rural poor often least well represented. High-resolution data on key social and health indicators are fundamental for targeting limited resources, especially where development funding has recently come under increased pressure. Globally, around 80% of countries regularly produce sex-disaggregated statistics at a ...

Abstract

Improved understanding of geographical variation and inequity in health status, wealth and access to resources within countries is increasingly being recognized as central to meeting development goals. Development and health indicators assessed at national or subnational scale can often conceal important inequities, with the rural poor often least well represented. The ability to target limited resources is fundamental, especially in an international context where funding for health and development comes under pressure. This has recently prompted the exploration of the potential ...

Abstract

[Excerpt: Datasets] The Demographic and Health Surveys (DHS) is a program of national household surveys implemented across a large number of LMICs. The DHS Program collects and analyses data on population demographic and health characteristics through more than 300 surveys in over 90 countries. The gender-disaggregated data we investigated in this report come from DHS datasets. [\n] [...] [Models specification] [::Bayesian model specification] The Gaussian Function (GF) in INLA is represented as a Gaussian Markov Random Function (GMRF). Computations in INLA are carried out using the GMRF by approximating a ...

Abstract

Tackling societal and environmental challenges requires new approaches that connect top-down global oversight with bottom-up subnational knowledge. We present a novel framework for participatory development of spatially explicit scenarios at national scale that model socioeconomic and environmental dynamics by reconciling local stakeholder perspectives and national spatial data. We illustrate results generated by this approach and evaluate its potential to contribute to a greater understanding of the relationship between development pathways and sustainability. Using the lens of land use and land cover ...

Abstract

List of indexed keywords within the transdisciplinary set of domains which relate to the Integrated Natural Resources Modelling and Management (INRMM). In particular, the list of keywords maps the semantic tags in the INRMM Meta-information Database (INRMM-MiD). [\n] The INRMM-MiD records providing this list are accessible by the special tag: inrmm-list-of-tags ( http://mfkp.org/INRMM/tag/inrmm-list-of-tags ). ...

This page of the database may be cited as: Integrated Natural Resources Modelling and Management - Meta-information Database. http://mfkp.org/INRMM/tag/tanzania

Publication metadata

Meta-information Database (INRMM-MiD).
This database integrates a dedicated meta-information database in CiteULike (the CiteULike INRMM Group) with the meta-information available in Google Scholar, CrossRef and DataCite. The Altmetric database with Article-Level Metrics is also harvested. Part of the provided semantic content (machine-readable) is made even human-readable thanks to the DCMI Dublin Core viewer. Digital preservation of the meta-information indexed within the INRMM-MiD publication records is implemented thanks to the Internet Archive.
The library of INRMM related pubblications may be quickly accessed with the following links.

Go to the INRMM Group in CiteULike. In this dedicated database editors may submit changes to the meta-information (login required). Inquiries may be sent to inrmm(at)maieutike.org.

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Full-text and abstracts of the publications indexed by the INRMM meta-information database are copyrighted by the respective publishers/authors. They are subject to all applicable copyright protection. The conditions of use of each indexed publication is defined by its copyright owner. Please, be aware that the indexed meta-information entirely relies on voluntary work and constitutes a quite incomplete and not homogeneous work-in-progress.
INRMM-MiD was experimentally established by the Maieutike Research Initiative in 2008 and then improved with the help of several volunteers (with a major technical upgrade in 2011). This new integrated interface is operational since 2014.